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Mobiles Crowdsourcing/sensing für die verbesserte Messung von subjektivem Sicherheitsempfinden im ÖP(N)V

Mobile crowd sourcing and sensing for the improved measurement of subjective perception of safety in public transport
: Restel, Hannes; Jendreck, Michael; Fuchs-Kittowski, Frank; Meissen, Ulrich; Klafft, Michael

Volltext (PDF; )

Fuchs-Kittowski, Frank (Hrsg.):
14. Fachgespräch "Ortsbezogene Anwendungen und Dienste" 2017. Tagungsband. Online resource : GI-Fachgruppe KuVS, Proceedings of the 14th Workshop on "Location-based applications and services", Berlin, Germany, September 21-22, 2017
Berlin, 2017 (CEUR Workshop Proceedings 2020)
Fachgespräch Ortsbezogene Anwendungen und Dienste (LBAS) <14, 2017, Berlin>
Workshop on Location-Based Applications and Services <14, 2017, Berlin>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()
MESM; crowdsourcing; crowdsensing; situative Befragung; Sicherheitsempfinden; ÖP(N)V

Securing safety in private and public spaces such as for public transport involves considerable costs. However, the effectiveness and benefits for safety are often disputed. The causality between the implementation of security measures and the occurrence of security-relevant events and perceived safety is not always obvious. A major contribution to improving this situation is to developtools for assessing public safety awareness and the impact of security measures on this perception. So far, individual parameters - such as the perception of safety - have been recorded only ex-ante and ex-post in the form of (paper-based) questionnaires and interviews. The research hypothesis of our approach is that the quality of the measurements can be significantly improved by the use of in-situ real-time queries and the inclusion of situational (especially location-dependent) factors. The presented solution combines classical electronic questionnaires with crowd sourcing/sensing approaches into a situational capture tool for subjective perception of safety. In contrast to static questionnaires, the collection of information can be dynamic, active, individual, and situational according to the location of the test person and environmental parameters. The implementation of our approach takes place in various stages of development, starting from a simple location- and time-based solution for controlling questions to the subjects to the final completion of a comprehensive situation detection. It combines a serverbased backend with the mobile detection tool: an app installed on smartphones, to which various sensors can be connected.